class_docstr: GLUE Model that Inherits from MegatronT5SFTModel and overrides the dataset building.
code: def _build_dataset(self, data_cfg, check_implict_grad_acc=False):
      if (
          check_implict_grad_acc
          and data_cfg.global_batch_size
          > data_cfg.micro_batch_size
          * parallel_state.get_data_parallel_world_size()
      ):
          raise ValueError(
              f'You are trying to use "implicit gradient accumulation" of {data_cfg.global_batch_size // (data_cfg.micro_batch_size * parallel_state.get_data_parallel_world_size())} in your validation/test datasets. This is not supported. Please set global_batch_size equal to micro_batch_size * data_parallel_world_size.'
          )
      if data_cfg.task_name == "xnli":
          dataset = TextToTextXNLIDataset(
              data_cfg.file_path,
              task_name=data_cfg.task_name,
              tokenizer=self.tokenizer,
              max_seq_length=data_cfg.max_seq_length,
              lang_list=self.cfg.eval_languages,
          )
      else:
          dataset = TextToTextGLUEDataset(
              data_cfg.file_path,
              task_name=data_cfg.task_name,
              tokenizer=self.tokenizer,
              max_seq_length=data_cfg.max_seq_length,
          )
      return dataset
  
file_path: NeMo/nemo/collections/nlp/models/language_modeling/megatron_glue_model.py
imports: from omegaconf.dictconfig import DictConfig
  from pytorch_lightning.trainer.trainer import Trainer
  from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import TextToTextGLUEDataset, TextToTextXNLIDataset
  from nemo.collections.nlp.models.language_modeling.megatron_t5_sft_model import MegatronT5SFTModel
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the MegatronT5GLUEModel obj's underlying member method code with example usage: obj._build_dataset(data_cfg, check_implict_grad_acc) and returns: dataset